Forward Deployed Engineer

Recruiting From Scratch•San Francisco, CA

10d•Onsite

About The Position

Our client is a rapidly growing AI company building fully managed digital workforces for enterprise customers. The company develops custom AI-agent systems that execute complex back-office workflows end-to-end, particularly across financial operations, compliance, and other business-critical processes. Rather than building simple copilots or point solutions, the platform combines AI agents, workflow automation, integrations, and ongoing operational support to deliver measurable business outcomes. The company has approximately 35 employees and has raised $15M in funding. The team is headquartered in North Beach, San Francisco, and operates with a high-ownership, low-bureaucracy culture. Engineers work directly with enterprise customers to understand their workflows, design solutions, build production systems, and maintain them after deployment. The engineering organization is intentionally flat, with significant autonomy and direct exposure to company leadership. This role sits at the intersection of software engineering, AI agents, customer-facing solution development, and production deployment. You will spend approximately 70% of your time on hands-on Python backend engineering and 30% working directly with customers and their technical teams.

Requirements

4–7 years of professional software engineering experience.
Strong experience as a customer-facing software engineer, forward deployed engineer, solutions engineer, or similar technical role.
Strong backend software engineering experience, ideally with Python.
Experience owning technical projects end-to-end from requirements through production deployment.
Experience working directly with customers or external technical stakeholders.
Experience building production systems rather than purely experimental or academic projects.
Experience working in fast-paced startup or high-growth environments.
Comfortable owning multiple projects and priorities simultaneously.
Strong Python development experience.
Strong backend engineering fundamentals.
Hands-on experience building applications or systems using LLMs.
Experience building AI agents, agentic workflows, or AI-powered automation systems.
Experience with RAG and retrieval-based systems.
Experience designing and integrating APIs and external systems.
Experience working with cloud infrastructure such as AWS, GCP, or Azure.
Strong understanding of production software engineering practices.
Ability to debug and troubleshoot complex technical systems.
Experience working with databases, services, APIs, and distributed systems.
Comfortable working across the stack when customer requirements demand it.
Experience translating ambiguous customer or business requirements into technical solutions.
Strong customer-facing communication skills.
Ability to understand complex business processes and identify opportunities for automation.
Experience integrating software into existing enterprise or legacy environments.
Comfortable explaining technical concepts to both technical and non-technical stakeholders.
Strong sense of product ownership and customer outcomes.
Experience operating systems in production where reliability and correctness matter.
Comfortable working in environments where customer requirements can change quickly.
High ownership and strong initiative.
Comfortable operating independently with minimal micromanagement.
Strong problem-solving and debugging abilities.
Excellent written and verbal communication.
Curious about emerging AI capabilities and eager to experiment.
Comfortable working directly with customers.
Able to balance technical depth with business context.
Adaptable and willing to work across different parts of the technical stack.
Strong project management and prioritization skills.
Comfortable in a high-expectation, high-performance environment.
Collaborative and able to work closely with engineering, AI strategy, and customer teams.

Responsibilities

Work directly with enterprise customers to understand complex business processes, technical requirements, and integration challenges.
Participate in customer discovery and translate business workflows into technical solutions.
Design and build custom AI-agent systems tailored to individual customer processes.
Work closely with AI strategists to scope, architect, and deliver customer solutions.
Communicate technical tradeoffs and implementation decisions clearly to customer technical teams.
Build trusted relationships with customers throughout implementation and ongoing production support.
Build production-grade AI-agent systems using Python, LLMs, RAG, and agentic workflows.
Develop backend services and integrations that allow AI agents to interact with enterprise systems.
Build workflows involving reasoning, decision-making, tool use, automation, and structured outputs.
Work with existing agent frameworks and internal infrastructure while contributing improvements where needed.
Develop reliable systems where accuracy, observability, and operational performance are critical.
Spend the majority of your time writing production-quality Python backend code.
Integrate AI-agent systems with customers' existing technology stacks and legacy systems.
Build APIs, services, data pipelines, and system integrations required for production deployments.
Troubleshoot complex technical issues across customer environments.
Take ownership of systems from initial solution design through deployment and ongoing maintenance.
Work across cloud environments including AWS, GCP, and Azure.
Ensure deployed systems are reliable, observable, and maintainable in production.
Own multiple customer projects simultaneously from discovery through production.
Drive projects forward independently with minimal oversight.
Identify technical risks and unblock projects proactively.
Partner with cross-functional teams to ensure customer implementations meet business and technical goals.
Participate in go-live processes and ongoing support for mission-critical customer workflows.
Continuously improve solutions based on customer feedback, production performance, and emerging AI capabilities.